Development and Analysis of Intelligent Recommendation System Using Machine Learning Approach

2020 ◽  
pp. 186-197 ◽  
Author(s):  
Pavlo Piletskiy ◽  
Dmytro Chumachenko ◽  
Ievgen Meniailov
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Approach ◽  
Machine Learning Approach

scholarly journals A Machine Learning Approach to Career Path Choice for Information Technology Graduates

2020 ◽  
Vol 10 (6) ◽  
pp. 6589-6596
Author(s):  
H. Al-Dossari ◽  
F. A. Nughaymish ◽  
Z. Al-Qahtani ◽  
M. Alkahlifah ◽  
A. Alqahtani
Keyword(s):  
Machine Learning ◽  
Information Technology ◽  
Recommendation System ◽  
Learning Algorithms ◽  
Career Path ◽  
Performance Comparison ◽  
Machine Learning Algorithms ◽  
Learning Approach ◽  
It Professionals ◽  
Machine Learning Approach

Enterprises rely more and more on well-qualified and highly specialized IT professionals. Although the increasing availability of IT jobs is a good indicator for IT graduates, they nonetheless may find themselves confused about the most appropriate career for their future. In this paper, a recommendation system called CareerRec is proposed, which uses machine learning algorithms to help IT graduates select a career path based on their skills. CareerRec was trained and tested using a dataset of 2255 employees in the IT sector in Saudi Arabia. We conducted a performance comparison between five machine learning algorithms to assess their accuracy for predicting the best-suited career path among 3 classes. Our experiments demonstrate that the XGBoost algorithm outperforms other models and gives the highest accuracy (70.47%).

scholarly journals Financial Inclusion: An Application of Machine Learning in Collaborative Filtering Recommender Systems

2020 ◽  
Vol 8 (6) ◽  
pp. 4243-4247
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Technologies ◽  
Financial Inclusion ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Current Scenario ◽  
Current Algorithm ◽  
The Impact

In the current scenario in finance, data play a major role for predicting stock market as well as verious financial instruments. For the estimation of financial data, the various algorithms and models have been used. The use of the advising method has been used in this paper. The advising programs are one of the main methodologies used in the present market scenario with machine learning technologies. This paper focuses on the impact of financial inclusion in Odisha using a machine learning approach such as the classification of kNearest Neighbors (k-NN). For financial inclusion systems, machine learning has become a commonly used method. The result takes into the ATMs, Banks and BCs ranking in different districts of Odisha. We used the k-Nearest Neighbor's machine learning methodology classification algorithm to characterize the recommendation system based on users of the mentioned populations. Using our approach we equate conventional collective filtering. Our results show that the linear algorithm is more reliable than the current algorithm and is more efficient and stable than current methods

scholarly journals A Machine Learning approach for automation of Resume Recommendation system

2020 ◽  
Vol 167 ◽  
pp. 2318-2327 ◽  
Author(s):  
Pradeep Kumar Roy ◽  
Sarabjeet Singh Chowdhary ◽  
Rocky Bhatia
Keyword(s):  
Machine Learning ◽  
Recommendation System ◽  
Learning Approach ◽  
Machine Learning Approach

1552-P: Machine Learning Approach to Decision-Making for Initial Insulin Use in Japanese Patients with Type 2 Diabetes

Diabetes ◽  
2020 ◽  
Vol 69 (Supplement 1) ◽  
pp. 1552-P
Author(s):  
KAZUYA FUJIHARA ◽  
MAYUKO H. YAMADA ◽  
YASUHIRO MATSUBAYASHI ◽  
MASAHIKO YAMAMOTO ◽  
TOSHIHIRO IIZUKA ◽  
...  
Keyword(s):  
Machine Learning ◽  
Type 2 Diabetes ◽  
Decision Making ◽  
Japanese Patients ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Insulin Use
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